Japanese laid-open patent publication number Hei 05-264467 describes a known pattern inspection method in which defects are detected by comparing an inspection image and a reference image.
In this method, a line sensor sequentially images an inspected object on which a repeated pattern is arranged in an orderly manner. A comparison is made with an image that has been delayed by a time interval corresponding to the pitch of the repeated pattern, and inconsistencies are detected as pattern defects. However, in practice the stage may vibrate or the inspected object may be tilted so that the two images are not always aligned. Thus, an offset must be determined for the image that has been delayed by the pitch of the repeated pattern. The two images are then aligned based on the determined offset. Then, differences between the images, e.g., luminance differences, are determined. If a difference is greater than a defined threshold value, it is determined to be a defect. Otherwise, it is determined to not be defective.
A standard alignment method for two images is an alignment method where the information from each of the full images are used to calculate an offset all at once. Problems related to this standard method are illustrated by FIGS. 1 and 2. FIG. 1 shows front-view drawings of examples of full images 110, 112, and 114 that tend to lead to failed alignments. FIG. 2 shows front-view drawings of examples of detection results 120, 122, 124 for full images that have failed alignments.
In comparative inspections, alignment of two full images is generally performed by using edges within the images as information for offset detection. An offset is calculated so that the edge offsets between the full images are minimized. There can be cases, as in FIG. 1 image 110 where there is only an elliptical pattern 21 at the right end of the image, in which there is very little edge information, i.e., the proportion of edges relative to the entire alignment region (hereinafter referred to as pattern density) is very low. There are other cases, as in FIG. 1 image 112 where there are many vertical edges 22 but there is only a rectangular pattern 23 oriented horizontally, in which edges are predominantly in a specific direction. There are other cases, as in FIG. 1 image 114 where there are many very small circular patterns 24 with only one black dot 25, in which a fine-pitch pattern dominates. In all of these cases, there is a high probability that an offset calculation error will be generated. Thus, methods that calculate offsets based on the information for entire images, as in the conventional technology, have a high probability that erroneous detections (false positives) will be generated by the offset.
The detection results from full images, such as in FIG. 1, with failed offsets are shown in FIG. 2 and will generate erroneous detections. FIG. 2 images 120, 122, 124 show erroneous detection positions 31, 32, 33, 34, 35. Furthermore, the possibility of errors is increased even more if there is luminance shading within an image or if there is uneven brightness between images. One method of reducing erroneous detections due to alignment is to lower the sensitivity. However, not all alignment errors cause a false detection. In addition lowering the sensitivity, lowers the detection rate for true defects. Thus unnecessary lowering of the sensitivity due to alignment errors should be avoided.
Thus there is a need for improved alignment techniques in which detection errors are minimally caused by alignment errors due to pattern density or shape, and/or for improved techniques for determining when the detection sensitivity needs to be lowered in comparison inspections.